With data explosion, it is likely there will be some information that will be misleading the analyst. With IoT and machine learning, the power of data is misused. Not being careful in detecting where the problem lies proves to be risky. You could be choosing chicken oil for business intelligence and analytics while the authentic figures are buried deep down. It is urgent to draw the line and AI and machine learning will be helpful.
This blog provides a brief overview of addressing the problem with flawed data. You can draw a line by examining why some custom apps fail and deliver inconsistent data.
Prevent unpleasant surprises
There is a need to collate data from all sources before it can be employed for business intelligence and analytics on a unified platform. As we know that some unpleasant surprises can crop up in the boardroom with confusing correlations. There are already countless examples of AI as a logical language being used for business intelligence. Take for instance the data being produced in a warehouse. Data emerges from all sorts of sources within the warehouse. There is no one repository in which it is assembled and then sent for analysis. All systems which are connected by various networks and cloud offer a barrage of information on a continuous basis. It is challenging for analysts to operate on such flowing streams of information sitting in a remote place. The complexity increases because of three reasons:
- The genuine source of data
- What part of the material is authentic and can be monitored for advising?
- How many members will have access to the information? Or will it be dispatched in silos to separate departments for further usage?
Addressing these three components will impact the decision making and the overall business intelligence and analytics for the corporate meetings. What is the solution to break free from this situation and fix the issues? This will support you to bridge the gaps between the IT and business strategies.
Also Read about: Carving Insights From Data: State of Analytics in 2018
How business leaders can work under cloud cover
Leaders are engaged and cannot be bothered with the streams of data. This is why cloud-based systems cater to them by plugging the required APIs on the boardroom dashboard. Machine Learning (ML) techniques can be employed to handle business decisions and compliances. ML is cleverly used for the network and app security. Since, it is not possible for humans to detect errors in emerging data, the programmed machine can red flag irregularities. The anomalies occur when the systems are attacked or crash due to overload. The data is bound to get corrupted and becomes useless. Then analysts cannot depend on the information. There are other asset management risks also that pose safety related problems. Such problems have increased people get their own devices to work or even relate to apps implemented on SaaS. As enterprises have proprietary information and business details on the networks, they cannot be compromised. Thus, infrastructure and the networks require enhancement to produce data that will not lie. Often systems configured with custom developed apps also tend to fail. They also crop up material that is not authentic. If customized apps have failed, your enterprise know which loop holes need to be plugged. They will obviously prove to be dangerous for business intelligence and analytics in the long run.
Recognize why some apps fail and relay garbled data
When you choose a service provider for devising a customized system, then the areas that are affected by wrong data relate to:
- Strategies for marketing to expand business. Many plans are built on data derived from previous records. They need to be checked for authentication before they are employed as the basis for planning campaigns. A level of accuracy is involved. Failure to obtain the right figures makes the research, and plans redundant.
- Many apps remain unrecognized because no one knows about them. Your customized app needs to be on a separate platform so it can acquire the real information to execute decisions. Evaluation of the weakness and the strengths of the apps present opportunities to make the data beneficial in the appropriate direction.
- Several platforms are available for apps. If your app is not functioning, then the platform needs to be changed. Or redesign it to adjust to the system.
If this blog has given food for thought, work on it. By now you must have realized what will work for you.
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